# import relevant libraries
import numpy as np
import os
import seaborn as sb
import requests
import pandas as pd
import tweepy
from tweepy import OAuthHandler
import json
from timeit import default_timer as timer

import matplotlib.pyplot as plt                                      # to plot graph
plt.rc("font", size=14)
%matplotlib inline
from matplotlib import cm as cm

import seaborn as sns                                                # for intractve graphs
sns.set(style="whitegrid", color_codes=True)

import warnings
warnings.filterwarnings('ignore')

Gathering Data¶

# load in twitter archive csv
archive_df = pd.read_csv('twitter-archive-enhanced.csv', encoding='utf-8')
archive_df.head(3)

archive_df.shape

(2356, 17)

# load in image prediction data from udacity servers
url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'
response = requests.get(url)

folder_name = 'image-predictions'
if not os.path.exists(folder_name):
    os.makedirs(folder_name)
    
with open(os.path.join(folder_name, url.split('/')[-1]), mode='wb') as file:
    file.write(response.content)

image_df = pd.read_csv(folder_name + '/image-predictions.tsv', sep='\t')
image_df.head(5)

# Query Twitter API for each tweet in the Twitter archive and save JSON in a text file
# These are hidden to comply with Twitter's API terms and conditions
consumer_key = 'XXXXXXX'
consumer_secret = 'XXXXXXX'
access_token = 'XXXXXXX'
access_secret = 'XXXXXXX'

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_secret)

api = tweepy.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)
api

<tweepy.api.API at 0x7f73b181bdd8>

tweet_ids = archive_df.tweet_id.values
len(tweet_ids)

2356

# Query Twitter's API for JSON data for each tweet ID in the Twitter archive
# api_data = []

# #read the created file
# with open("tweet-json.txt", "r") as f:
#     for line in f:
#         try: 
#             tweet = json.loads(line)
#             #append a dictionary to the created list            
#             api_data.append({
#                 "tweet_id": tweet["id"],
#                 "retweet_count": tweet["retweet_count"],
#                 "favorite_count": tweet["favorite_count"],
#                 "retweeted": tweet["retweeted"],
#                 "display_text_range": tweet["display_text_range"]                
#             })               
                
#             #tweet["entities"]["media"][0]["media_url"]
#         except:
#             print("Error.") 
            
# df_api = pd.DataFrame(api_data, columns = ["tweet_id", "retweet_count", "favorite_count", "retweeted", "display_text_range"])
# df_api.head()

# read this tweet_json.txt file line by line into a pandas DataFrame with (at minimum) 
# tweet ID, retweet count, and favorite count."
df_tweet_json = pd.DataFrame(columns=['tweet_id', 'retweet_count', 'favorite_count'])
with open('tweet-json.txt') as data_file:
    for line in data_file:
        tweet = json.loads(line)
        tweet_id = tweet['id_str']
        retweet_count = tweet['retweet_count']
        favorite_count = tweet['favorite_count']
        df_tweet_json = df_tweet_json.append(pd.DataFrame([[tweet_id, retweet_count, favorite_count]],
        columns=['tweet_id', 'retweet_count', 'favorite_count']))
        df_tweet_json = df_tweet_json.reset_index(drop=True)

df_tweet_json.head()

Assess data¶

archive DF

archive_df.head(20)

archive_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
tweet_id                      2356 non-null int64
in_reply_to_status_id         78 non-null float64
in_reply_to_user_id           78 non-null float64
timestamp                     2356 non-null object
source                        2356 non-null object
text                          2356 non-null object
retweeted_status_id           181 non-null float64
retweeted_status_user_id      181 non-null float64
retweeted_status_timestamp    181 non-null object
expanded_urls                 2297 non-null object
rating_numerator              2356 non-null int64
rating_denominator            2356 non-null int64
name                          2356 non-null object
doggo                         2356 non-null object
floofer                       2356 non-null object
pupper                        2356 non-null object
puppo                         2356 non-null object
dtypes: float64(4), int64(3), object(10)
memory usage: 313.0+ KB

timestamp and retweeted_status_timestamp is an object

# check for duplicates
archive_df[archive_df.duplicated()].sum()

tweet_id                      0.0
in_reply_to_status_id         0.0
in_reply_to_user_id           0.0
timestamp                     0.0
source                        0.0
text                          0.0
retweeted_status_id           0.0
retweeted_status_user_id      0.0
retweeted_status_timestamp    0.0
expanded_urls                 0.0
rating_numerator              0.0
rating_denominator            0.0
name                          0.0
doggo                         0.0
floofer                       0.0
pupper                        0.0
puppo                         0.0
dtype: float64

# check for missing values 
archive_df.isnull().sum()

tweet_id                         0
in_reply_to_status_id         2278
in_reply_to_user_id           2278
timestamp                        0
source                           0
text                             0
retweeted_status_id           2175
retweeted_status_user_id      2175
retweeted_status_timestamp    2175
expanded_urls                   59
rating_numerator                 0
rating_denominator               0
name                             0
doggo                            0
floofer                          0
pupper                           0
puppo                            0
dtype: int64

there are missing values in in_reply_to_status_id ,in_reply_to_user_id,retweeted_status_id,retweeted_status_user_id,retweeted_status_timestamp columns

pd.set_option('display.max_rows', 2000)
archive_df.name[archive_df.name.str.islower()]

22              such
56                 a
118            quite
169            quite
193            quite
335              not
369              one
542       incredibly
649                a
682              mad
759               an
773             very
801                a
819             very
822             just
852               my
924              one
988              not
992              his
993              one
1002               a
1004               a
1017               a
1025              an
1031            very
1040        actually
1049               a
1063            just
1071         getting
1095             mad
1097            very
1120            this
1121    unacceptable
1138             all
1193               a
1206             old
1207               a
1259     infuriating
1340               a
1351               a
1361               a
1362              an
1368               a
1382               a
1385            very
1435         getting
1457            just
1499               a
1527             the
1603             the
1693        actually
1724              by
1737               a
1747      officially
1785               a
1797             the
1815             the
1853               a
1854               a
1877               a
1878               a
1916            life
1923               a
1936             one
1941               a
1955               a
1994               a
2001           light
2019            just
2030           space
2034               a
2037             the
2066               a
2116               a
2125               a
2128               a
2146               a
2153               a
2161               a
2191               a
2198               a
2204              an
2211               a
2212             the
2218               a
2222               a
2235               a
2249               a
2255               a
2264               a
2273               a
2287               a
2304               a
2311               a
2314               a
2326           quite
2327               a
2333              an
2334               a
2335              an
2345             the
2346             the
2347               a
2348               a
2349              an
2350               a
2352               a
2353               a
2354               a
Name: name, dtype: object

archive_df.pupper.value_counts()

None      2099
pupper     257
Name: pupper, dtype: int64

 archive_df.doggo.value_counts()

None     2259
doggo      97
Name: doggo, dtype: int64

archive_df.floofer.value_counts()

None       2346
floofer      10
Name: floofer, dtype: int64

archive_df.rating_denominator.value_counts()

10     2333
11        3
50        3
80        2
20        2
2         1
16        1
40        1
70        1
15        1
90        1
110       1
120       1
130       1
150       1
170       1
7         1
0         1
Name: rating_denominator, dtype: int64

archive_df.rating_numerator.value_counts()

12      558
11      464
10      461
13      351
9       158
8       102
7        55
14       54
5        37
6        32
3        19
4        17
1         9
2         9
420       2
0         2
15        2
75        2
80        1
20        1
24        1
26        1
44        1
50        1
60        1
165       1
84        1
88        1
144       1
182       1
143       1
666       1
960       1
1776      1
17        1
27        1
45        1
99        1
121       1
204       1
Name: rating_numerator, dtype: int64

#identify the rating denominator with 0
archive_df[archive_df['rating_denominator'] == 0]

Quality issues- archive df

there are missing values in in_reply_to_status_id ,in_reply_to_user_id,retweeted_status_id,retweeted_status_user_id,retweeted_status_timestamp columns
timestamp and retweeted_status_timestamp is an object instead of a datetime object

there are weird dog names in name columns like,

  'a', 'actually', 'all', 'an', 'by', 
  'getting', 'his',
 'incredibly', 'infuriating', 'just', 'life', 'light', 'mad', 'my',
 'not', 'officially', 'old', 'one', 'quite', 'space', 'such', 'the',
 'this', 'unacceptable', 'very.

rating denominator has a value of 0
rating denominator and numerator have high inconsistent values as high as 1776 and 170
missing values in doggo, floofer, pupper , puppo columns - has None instead of NaN
in_reply_to_status_id, in_reply_to_user_id, retweeted_status_id, retweeted_status_user_id must be integers instead of float ( They have id's similar to tweet_id)

Tidiness issues - archive df

Dog stages are found in multiple columns, hence we should find a way to club all these variables into single column. This will reduce the dimensionality of the dataframe

json tweet data¶

df_tweet_json.head()

df_tweet_json.describe()

df_tweet_json.isnull().sum()

tweet_id          0
retweet_count     0
favorite_count    0
dtype: int64

df_tweet_json.tweet_id.shape, archive_df.tweet_id.shape

((2354,), (2356,))

df_tweet_json.tweet_id.duplicated().sum()

0

df_tweet_json.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2354 entries, 0 to 2353
Data columns (total 3 columns):
tweet_id          2354 non-null object
retweet_count     2354 non-null object
favorite_count    2354 non-null object
dtypes: object(3)
memory usage: 55.2+ KB

quality issues - json data

there are data missing

tidiness issues - json data

data should be joined with archive data
data type for tweet_id retweet count, favorite count are object instead of integers

image prediction data¶

#preview the data
image_df.head(5)

image_df.shape

(2075, 12)

image_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2075 entries, 0 to 2074
Data columns (total 12 columns):
tweet_id    2075 non-null int64
jpg_url     2075 non-null object
img_num     2075 non-null int64
p1          2075 non-null object
p1_conf     2075 non-null float64
p1_dog      2075 non-null bool
p2          2075 non-null object
p2_conf     2075 non-null float64
p2_dog      2075 non-null bool
p3          2075 non-null object
p3_conf     2075 non-null float64
p3_dog      2075 non-null bool
dtypes: bool(3), float64(3), int64(2), object(4)
memory usage: 152.1+ KB

image_df.describe()

# investigate duplicates
image_df.duplicated().sum()

0

# investigate missing data
image_df.isnull().sum().sum()

0

# using heatmap investigate for missing values
sb.heatmap(image_df.isnull())

<matplotlib.axes._subplots.AxesSubplot at 0x7f73b1254048>

Quality - image_predictions_df dataset:

since this data belongs to archive df it means that only 2075 tweetIds in our achive data have images

Tidiness - image prediction data

All the prediction outputs from different algorithms have to be joined with archive_df, becuase all the tweets information is found with archive_df

Clean¶

# c
archive_clean = archive_df.copy()
image_df_clean = image_df.copy()
tweet_json_clean = df_tweet_json.copy()

convert data types¶

Define¶

change the datatype of tweet_id, retweet_count. favorite_counts in tweet_json_clean from object to integar

Code¶

cols= list(tweet_json_clean.columns)
cols

['tweet_id', 'retweet_count', 'favorite_count']

tweet_json_clean= tweet_json_clean[cols].astype('int64')

test¶

tweet_json_clean.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2354 entries, 0 to 2353
Data columns (total 3 columns):
tweet_id          2354 non-null int64
retweet_count     2354 non-null int64
favorite_count    2354 non-null int64
dtypes: int64(3)
memory usage: 55.2 KB

Tidiness¶

Dog stages are found in multiple columns, hence we should find a way to club all these variables into single column. This will reduce the dimensionality of the dataframe

define¶

Convert the dog stage or category into one column instead of the multiple columns

Code¶

archive_clean.columns

Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',
       'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',
       'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',
       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo'],
      dtype='object')

archive_clean = pd.melt(archive_clean,id_vars=['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',
       'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',
       'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',
       'rating_denominator', 'name'],
                        var_name = 'dog_stage',value_name = 'dog_stage_cat')
archive_clean = archive_clean.drop('dog_stage_cat', axis=1)

Test¶

archive_clean.head()

archive_clean[archive_clean.dog_stage=='doggo']

image_df and tweet_json_clean should be part of archive_clean table

Define

Merge image_df and tweet_json_clean with archive_clean

Code

main_df =  pd.merge(archive_clean, image_df_clean,
                            on=['tweet_id'], how='inner')
main_df =  pd.merge(main_df, tweet_json_clean,
                            on=['tweet_id'], how='inner')

Test

pd.set_option('display.max_columns',1000)
main_df.head()

main_df[main_df.tweet_id.duplicated()]

Missing data¶

Define¶

drop columns with NANs

Code¶

main_df.isnull().sum()

tweet_id                         0
in_reply_to_status_id         8200
in_reply_to_user_id           8200
timestamp                        0
source                           0
text                             0
retweeted_status_id           7976
retweeted_status_user_id      7976
retweeted_status_timestamp    7976
expanded_urls                    0
rating_numerator                 0
rating_denominator               0
name                             0
dog_stage                        0
jpg_url                          0
img_num                          0
p1                               0
p1_conf                          0
p1_dog                           0
p2                               0
p2_conf                          0
p2_dog                           0
p3                               0
p3_conf                          0
p3_dog                           0
retweet_count                    0
favorite_count                   0
dtype: int64

col_drop = ['retweeted_status_id','retweeted_status_user_id','retweeted_status_timestamp',
            'in_reply_to_status_id','in_reply_to_user_id']
main_df.drop(columns=col_drop,inplace=True,axis=1)

main_df

Test

print(main_df.isnull().sum())

tweet_id              0
timestamp             0
source                0
text                  0
expanded_urls         0
rating_numerator      0
rating_denominator    0
name                  0
dog_stage             0
jpg_url               0
img_num               0
p1                    0
p1_conf               0
p1_dog                0
p2                    0
p2_conf               0
p2_dog                0
p3                    0
p3_conf               0
p3_dog                0
retweet_count         0
favorite_count        0
dtype: int64

main_df.shape

(8292, 22)

main_df.head()

Define

convert timestamp from object to datetime
convert dog_state_cat to categorical variable
convert tweet_id to object

Code

main_df['timestamp'] = pd.to_datetime(main_df['timestamp'], format='%Y-%m-%d %H:%M:%S')
main_df['dog_stage'] = main_df['dog_stage'].astype('category')
main_df['tweet_id'] = main_df['tweet_id'].astype('object')
main_df['retweet_count'] = main_df['retweet_count'].astype('int64')
main_df['favorite_count'] = main_df['favorite_count'].astype('int64')

Test

main_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 8292 entries, 0 to 8291
Data columns (total 22 columns):
tweet_id              8292 non-null object
timestamp             8292 non-null datetime64[ns]
source                8292 non-null object
text                  8292 non-null object
expanded_urls         8292 non-null object
rating_numerator      8292 non-null int64
rating_denominator    8292 non-null int64
name                  8292 non-null object
dog_stage             8292 non-null category
jpg_url               8292 non-null object
img_num               8292 non-null int64
p1                    8292 non-null object
p1_conf               8292 non-null float64
p1_dog                8292 non-null bool
p2                    8292 non-null object
p2_conf               8292 non-null float64
p2_dog                8292 non-null bool
p3                    8292 non-null object
p3_conf               8292 non-null float64
p3_dog                8292 non-null bool
retweet_count         8292 non-null int64
favorite_count        8292 non-null int64
dtypes: bool(3), category(1), datetime64[ns](1), float64(3), int64(5), object(9)
memory usage: 1.2+ MB

Define

remove wrong dog names
Extract correct numerator and denominator rfrom text column
standardizing to a denominator of 10 for groups of dogs

Code

list(main_df.name[main_df.name.str.islower()].unique())

['such',
 'a',
 'quite',
 'one',
 'incredibly',
 'an',
 'very',
 'just',
 'my',
 'not',
 'his',
 'getting',
 'this',
 'unacceptable',
 'all',
 'infuriating',
 'the',
 'actually',
 'by',
 'officially',
 'light',
 'space']

# remove wrong dog names
words =list(main_df.name[main_df.name.str.islower()].unique())
main_df['name'] = main_df['name'].apply(lambda x : np.nan if x in words else x)
main_df['name'] = main_df['name'].apply(lambda x : np.nan if x =='None' else x)
main_df = main_df.dropna()
main_df.name.isna().sum()

0

# using regex to extract the correct dog rating
ratings = main_df.text.str.extract('((?:\d+\.)?\d+)\/(\d+)', expand=True)

main_df.rating_numerator = ratings
main_df['rating_numerator'] = main_df['rating_numerator'].astype('float64')

# standardizing to a denominator of 10 for groups of dogs:

rating_num = [int(round(num/(denom/10)))  if denom != 10 and num/denom <= 2 
                           else num for num, denom in zip(main_df['rating_numerator'], main_df['rating_denominator'])]
rating_denom = [10 if denom != 10 and num/denom <= 2
                             else denom for num, denom in zip(main_df['rating_numerator'], main_df['rating_denominator'])]
main_df['rating_numerator'] = rating_num
main_df['rating_denominator'] = rating_denom

main_df = main_df.drop(main_df[((main_df['rating_denominator'] != 10) | (main_df['rating_numerator'] > 20))].index)

Test

main_df.head(20)

main_df.name.value_counts()

Charlie           44
Tucker            40
Cooper            40
Penny             40
Lucy              40
Oliver            40
Sadie             32
Lola              32
Winston           32
Bo                32
Daisy             28
Toby              28
Rusty             24
Bella             24
Koda              24
Stanley           24
Bailey            24
Scout             24
Dave              24
Jax               24
Milo              24
Leo               20
Alfie             20
Louis             20
Oscar             20
Larry             20
Chester           20
Buddy             20
Bear              16
Bentley           16
Scooter           16
Phil              16
Reggie            16
Derek             16
Sunny             16
Bruce             16
Dexter            16
Chip              16
Gary              16
Clark             16
Finn              16
Cassie            16
Jack              16
Duke              16
Clarence          16
Maggie            16
Gus               16
Winnie            16
Oakley            16
Ruby              16
Loki              16
Jerry             16
Brody             16
Archie            16
Sophie            16
George            16
Walter            16
Ellie             12
Maximus           12
Vincent           12
Jeffrey           12
Rosie             12
Malcolm           12
Samson            12
Gerald            12
Wyatt             12
Colby             12
Frankie           12
Kyle              12
Moose             12
Max               12
Paisley           12
Waffles           12
Louie             12
Sammy             12
Calvin            12
Gizmo             12
Otis              12
Lily              12
Beau              12
Sebastian         12
Boomer            12
Olive             12
Nala              12
Mia               12
Wallace           12
Peaches           12
Sampson           12
Zoey              12
Hank              12
Earl              12
Jimothy           12
Reese             12
Shadow            12
Riley             12
Zeke              12
Steven            12
Wilson            12
Bell               8
Chuckles           8
Sugar              8
Ava                8
Rubio              8
Juno               8
Aspen              8
Oliviér            8
Kenneth            8
Phred              8
Raymond            8
Thumas             8
Ollie              8
Panda              8
Calbert            8
Hobbes             8
Patrick            8
Ash                8
Chipson            8
Curtis             8
Arnie              8
Jimison            8
Franklin           8
Lilly              8
Klein              8
Alice              8
Sansa              8
Linda              8
Coops              8
Happy              8
Kreg               8
Griffin            8
Herm               8
Piper              8
Rufus              8
Roosevelt          8
Eli                8
Mister             8
Klevin             8
Dash               8
Smokey             8
Rizzy              8
Sarge              8
Yogi               8
Opal               8
Herald             8
Moreton            8
Luna               8
Doug               8
Chet               8
Davey              8
Odie               8
Paull              8
Terry              8
Coco               8
Jiminy             8
Keith              8
Django             8
Kirby              8
Elliot             8
Baxter             8
Cash               8
Albert             8
Rocky              8
Jeph               8
Harper             8
Theodore           8
Chelsea            8
CeCe               8
Bisquick           8
Luca               8
Astrid             8
Atlas              8
Phineas            8
Lenny              8
Bob                8
Benji              8
Gabe               8
Indie              8
Marley             8
Crystal            8
Ken                8
Lincoln            8
Penelope           8
Rory               8
Tyr                8
Solomon            8
Dakota             8
Frank              8
Misty              8
Abby               8
Kevin              8
Jackson            8
Axel               8
Carl               8
Butter             8
Trooper            8
Charles            8
Lennon             8
Chompsky           8
Percy              8
Bernie             8
Fiona              8
Wally              8
Watson             8
Kreggory           8
Brad               8
Albus              8
Neptune            8
Remington          8
Churlie            8
Belle              8
Moe                8
Pippa              8
Finley             8
Ted                8
Harold             8
Hammond            8
Stubert            8
Cali               8
Pickles            8
Hunter             8
Romeo              8
Titan              8
Gromit             8
Kenny              8
Lou                8
Sandy              8
Betty              8
Reginald           8
Hercules           8
Cody               8
Doc                8
Bubbles            8
Jesse              8
Benedict           8
Nelly              8
Flávio             8
Stephan            8
Maxaroni           8
Lorenzo            8
Kilo               8
Olivia             8
Cupcake            8
Mollie             4
Peanut             4
Timber             4
Goose              4
Farfle             4
Michelangelope     4
Millie             4
Kallie             4
Shawwn             4
Brooks             4
Orion              4
Arnold             4
Harlso             4
Carll              4
Glacier            4
Sojourner          4
Nimbus             4
Lolo               4
Finnegus           4
Dotsy              4
JD                 4
Ronnie             4
Tuco               4
Trip               4
Leela              4
Boots              4
Smiley             4
Eazy               4
Rumble             4
Carly              4
Herschel           4
Harry              4
Mark               4
Callie             4
Alexander          4
Clarq              4
Brandi             4
Pupcasso           4
Richie             4
Duchess            4
Donny              4
Odin               4
Ashleigh           4
Loomis             4
Tonks              4
Tassy              4
Cannon             4
Lillie             4
Fletcher           4
Opie               4
Brudge             4
Kendall            4
Al                 4
Theo               4
Lucia              4
Kaia               4
Moofasa            4
Stephanus          4
Koko               4
BeBe               4
Hubertson          4
Gidget             4
Bloop              4
Strudel            4
Stella             4
Emmie              4
Sprinkles          4
Newt               4
Shadoe             4
Sundance           4
Acro               4
Leonard            4
Chadrick           4
Traviss            4
Wiggles            4
Tango              4
Birf               4
Livvie             4
Sailer             4
Luther             4
Meyer              4
Ridley             4
Blakely            4
Strider            4
Mairi              4
Poppy              4
Flash              4
Jay                4
Chloe              4
Aldrick            4
Ace                4
Spanky             4
Fizz               4
Marvin             4
Jaycob             4
Brat               4
Lance              4
Enchilada          4
Vinnie             4
Willem             4
Crumpet            4
Craig              4
Taco               4
Anna               4
Jerome             4
Ed                 4
Shnuggles          4
Rolf               4
Pip                4
Lulu               4
Raphael            4
Noah               4
Nigel              4
Asher              4
Tayzie             4
Gin                4
Lucky              4
Harvey             4
Crawford           4
Rinna              4
Aqua               4
Marlee             4
Danny              4
Iggy               4
Beemo              4
Crimson            4
Rodman             4
Tobi               4
Philbert           4
Reptar             4
Gustav             4
Gerbald            4
Vince              4
Arlen              4
Bubba              4
Snoop              4
Jo                 4
Noosh              4
Alfy               4
Ronduh             4
Wishes             4
Jazzy              4
Pilot              4
Gustaf             4
Creg               4
Franq              4
Ito                4
Duddles            4
Jeffrie            4
Doobert            4
Mona               4
Maude              4
Bronte             4
Augie              4
Kobe               4
Brady              4
Gordon             4
Dex                4
Hurley             4
Malikai            4
Sephie             4
Herb               4
Eve                4
Venti              4
Ralf               4
Bobb               4
Devón              4
Tupawc             4
Tyrus              4
Jarvis             4
Mason              4
Boston             4
Apollo             4
Adele              4
Grady              4
Covach             4
Tuck               4
Claude             4
Mattie             4
Meatball           4
Karma              4
Jordy              4
Miley              4
Comet              4
Karll              4
Rambo              4
Sora               4
Bungalo            4
Sailor             4
Holly              4
Kloey              4
Josep              4
Godi               4
Jomathan           4
Georgie            4
Eevee              4
Binky              4
Sandra             4
Mauve              4
Ozzy               4
Zeek               4
Schnozz            4
Zuzu               4
Shelby             4
Pippin             4
Dug                4
Sam                4
Kial               4
Butters            4
Swagger            4
Naphaniel          4
Barney             4
Geoff              4
Jazz               4
Derby              4
Biden              4
Tito               4
Erik               4
Ziva               4
Rey                4
Rodney             4
Hamrick            4
Akumi              4
William            4
Carter             4
Thor               4
Alexanderson       4
Perry              4
Fred               4
Dwight             4
Bilbo              4
Stu                4
Alf                4
Rufio              4
Lassie             4
Shikha             4
Dale               4
Freddery           4
Lenox              4
Iroh               4
Ralphus            4
Skittles           4
Jessiga            4
Grizzie            4
Bonaparte          4
Dante              4
Dudley             4
Ruffles            4
Jangle             4
Pablo              4
Cora               4
Jackie             4
Pinot              4
Clifford           4
Norman             4
Aubie              4
Kara               4
Ralph              4
Lilli              4
Toffee             4
Sprout             4
Amy                4
Mya                4
Jebberson          4
Bertson            4
Billy              4
Amber              4
Blitz              4
Kollin             4
Caryl              4
Rumpole            4
Blanket            4
Pete               4
Kuyu               4
Jett               4
Andru              4
Jennifur           4
Edd                4
Humphrey           4
Tripp              4
Coleman            4
Crouton            4
Tebow              4
Barclay            4
Willy              4
Emma               4
Berb               4
Billl              4
Mimosa             4
Nugget             4
Lupe               4
Winifred           4
Mike               4
Bruno              4
Bobble             4
Kellogg            4
Kaiya              4
Jeffri             4
Hall               4
DonDon             4
Batdog             4
Gunner             4
Karl               4
Mojo               4
Timison            4
Aiden              4
Arya               4
Colin              4
Mabel              4
Kayla              4
Beebop             4
Pavlov             4
Sobe               4
Ulysses            4
Rooney             4
Huck               4
Chase              4
Juckson            4
Laika              4
Hazel              4
Harrison           4
Walker             4
Grizz              4
Rueben             4
Schnitzel          4
Storkson           4
Saydee             4
Fynn               4
Fiji               4
Ole                4
Chesterson         4
Jimbo              4
Clarkus            4
Edmund             4
Tedders            4
Robin              4
Stefan             4
Rocco              4
Pipsy              4
Oreo               4
Sierra             4
Shiloh             4
Torque             4
Olaf               4
Superpup           4
Ike                4
Layla              4
Kanu               4
Bradlay            4
Philippe           4
Dutch              4
Dylan              4
Cedrick            4
Logan              4
Vixen              4
Evy                4
Chesney            4
Pumpkin            4
Remy               4
Severus            4
Flurpson           4
Julio              4
Champ              4
Mitch              4
Bode               4
Pawnd              4
Hero               4
Kenzie             4
Lorelei            4
Snoopy             4
Fido               4
Stormy             4
Andy               4
Chaz               4
Charleson          4
Mutt               4
Lipton             4
Timmy              4
Gert               4
Charl              4
Cuddles            4
Jessifer           4
Chubbs             4
Rilo               4
Scruffers          4
Bodie              4
Buddah             4
Levi               4
Sid                4
Eleanor            4
Yoda               4
Heinrich           4
Joey               4
Tater              4
Darby              4
Snickers           4
Rontu              4
Furzey             4
Jim                4
Monster            4
Jareld             4
Major              4
Bowie              4
Miguel             4
Bluebert           4
Bradley            4
Milky              4
Maddie             4
Tino               4
Bobbay             4
Diogi              4
Darla              4
Cecil              4
River              4
Lacy               4
Zoe                4
Jiminus            4
Glenn              4
Kevon              4
Sunshine           4
Striker            4
Carper             4
Blue               4
Suki               4
Tess               4
Brandy             4
Blu                4
Chuq               4
Kane               4
Fillup             4
Terrenth           4
DayZ               4
Patch              4
Murphy             4
Rover              4
Dewey              4
Brandonald         4
Einstein           4
Emmy               4
Molly              4
Napolean           4
Kyro               4
Blipson            4
Cilantro           4
Darrel             4
Katie              4
Lambeau            4
Shakespeare        4
Tom                4
Dido               4
Huxley             4
Baron              4
Ozzie              4
Ralphson           4
Frönq              4
Cheesy             4
Randall            4
Ralphie            4
Fwed               4
Jaspers            4
Maks               4
Obi                4
Bert               4
Clyde              4
Dot                4
Tiger              4
Kathmandu          4
Horace             4
Jersey             4
Ginger             4
Autumn             4
Baloo              4
Obie               4
Edgar              4
Laela              4
Sky                4
Stewie             4
Chuck              4
Willow             4
Bobby              4
Cleopatricia       4
Zooey              4
Bruiser            4
Damon              4
Brutus             4
Monkey             4
Divine             4
Ralpher            4
Tanner             4
Trigger            4
Benny              4
Aja                4
Angel              4
Dietrich           4
Lilah              4
Joshwa             4
Antony             4
Bloo               4
Canela             4
Sparky             4
Sage               4
Banditt            4
Liam               4
Travis             4
Stark              4
Trevith            4
Mary               4
Brownie            4
Samsom             4
Dixie              4
Tycho              4
Keet               4
Cupid              4
Rose               4
Wesley             4
Spark              4
Corey              4
Monty              4
Eugene             4
Harnold            4
Cal                4
Deacon             4
Socks              4
Tommy              4
Filup              4
Dawn               4
Julius             4
Pubert             4
Godzilla           4
Brockly            4
Jarod              4
Reagan             4
Henry              4
Buckley            4
Kody               4
Griswold           4
Tug                4
Dook               4
Shaggy             4
Anthony            4
Kingsley           4
Hector             4
Durg               4
Willie             4
Nida               4
Skye               4
Kawhi              4
Halo               4
Arlo               4
Daniel             4
Roscoe             4
Tyrone             4
Gilbert            4
Ester              4
Scott              4
Lili               4
Dobby              4
Beya               4
Eriq               4
Tilly              4
Lizzie             4
Leonidas           4
Oshie              4
Carbon             4
Dallas             4
Timofy             4
Nico               4
Siba               4
Zara               4
Nollie             4
Terrance           4
Ricky              4
Steve              4
O                  4
Cermet             4
Hermione           4
Chevy              4
Jockson            4
Bayley             4
Berkeley           4
Burt               4
Rupert             4
Shooter            4
Lugan              4
Kulet              4
Bauer              4
Bones              4
Ralphé             4
Keurig             4
Sweet              4
Beckham            4
Cheryl             4
Mac                4
Mosby              4
Hanz               4
Clybe              4
Marty              4
Taz                4
Pepper             4
Tove               4
Howie              4
Maya               4
Snicku             4
Fabio              4
Ivar               4
Amélie             4
Grey               4
Pancake            4
Geno               4
Atticus            4
Anakin             4
Banjo              4
Puff               4
Kirk               4
Tessa              4
Stuart             4
Kota               4
Rorie              4
Jameson            4
Mingus             4
General            4
Petrick            4
Ambrose            4
Bookstore          4
Alejandro          4
Margo              4
Maxwell            4
Emanuel            4
Pluto              4
Maisey             4
Jamesy             4
Longfellow         4
Chef               4
Sonny              4
Link               4
Christoper         4
Staniel            4
Grizzwald          4
Coopson            4
Mack               4
Brian              4
Sully              4
Tedrick            4
Simba              4
Ebby               4
Meera              4
Wafer              4
Gòrdón             4
Mo                 4
Jonah              4
Ron                4
Jeb                4
Kramer             4
Jed                4
Vinscent           4
Jeremy             4
Rizzo              4
Genevieve          4
Florence           4
Remus              4
Ben                4
Mookie             4
Zeus               4
Rhino              4
Quinn              4
Marq               4
Todo               4
Dunkin             4
Goliath            4
Kona               4
Spencer            4
Name: name, dtype: int64

Define

extract the source the tweet was posted from in the source column

code

href = main_df["source"].str.split('"', expand = True)[1].str.split('/',expand=True)[4]
href
main_df["source"] = href

Test

main_df.source.value_counts()

iphone       5480
tweetdeck      32
Name: source, dtype: int64

main_df.head()

main_df = main_df.dropna()

Saving into the main dataframe, Analysis, Visualization and communicating findings¶

# storing main dataframe as csv
main_df.to_csv('main_df.csv', encoding='utf-8', index=False)

# Read in the created data
df = pd.read_csv('main_df.csv')
df.head()

df.p1.value_counts()

golden_retriever                  388
Labrador_retriever                264
Pembroke                          252
Chihuahua                         236
pug                               164
chow                              136
toy_poodle                        112
Samoyed                           100
Pomeranian                         96
malamute                           92
French_bulldog                     88
cocker_spaniel                     84
Siberian_husky                     76
miniature_pinscher                 68
Staffordshire_bullterrier          68
Chesapeake_Bay_retriever           64
Cardigan                           64
seat_belt                          64
Shih-Tzu                           60
Shetland_sheepdog                  56
German_shepherd                    56
beagle                             52
Eskimo_dog                         48
teddy                              44
Pekinese                           44
Border_collie                      40
Italian_greyhound                  40
vizsla                             40
basset                             40
collie                             40
American_Staffordshire_terrier     40
Bernese_mountain_dog               36
boxer                              36
Lakeland_terrier                   36
Rottweiler                         36
Maltese_dog                        36
Airedale                           36
Old_English_sheepdog               36
schipperke                         36
Doberman                           32
kelpie                             32
kuvasz                             32
Great_Pyrenees                     32
Blenheim_spaniel                   32
dingo                              28
Boston_bull                        28
Norwegian_elkhound                 28
whippet                            28
tub                                28
miniature_poodle                   28
malinois                           24
bath_towel                         24
dalmatian                          24
English_setter                     24
Border_terrier                     24
West_Highland_white_terrier        24
standard_poodle                    24
papillon                           24
Yorkshire_terrier                  24
Brittany_spaniel                   24
Great_Dane                         24
Siamese_cat                        24
Saint_Bernard                      20
Norfolk_terrier                    20
English_springer                   20
ice_bear                           20
bloodhound                         20
soft-coated_wheaten_terrier        20
tennis_ball                        20
web_site                           16
porcupine                          16
doormat                            16
hamster                            16
bluetick                           16
Walker_hound                       16
miniature_schnauzer                16
keeshond                           16
swing                              16
minivan                            16
borzoi                             16
Irish_setter                       16
bull_mastiff                       16
Dandie_Dinmont                     16
llama                              16
window_shade                       12
jack-o'-lantern                    12
ram                                12
home_theater                       12
Weimaraner                         12
Irish_water_spaniel                12
shopping_cart                      12
Mexican_hairless                   12
barrow                             12
hog                                12
curly-coated_retriever             12
motor_scooter                      12
cairn                              12
German_short-haired_pointer        12
Tibetan_terrier                    12
Bedlington_terrier                 12
Saluki                             12
refrigerator                       12
flat-coated_retriever              12
dishwasher                         12
Newfoundland                       12
hippopotamus                       12
bathtub                            12
Norwich_terrier                    12
Afghan_hound                       12
car_mirror                         12
mousetrap                          12
street_sign                         8
Australian_terrier                  8
acorn_squash                        8
Greater_Swiss_Mountain_dog          8
muzzle                              8
wombat                              8
redbone                             8
Tibetan_mastiff                     8
balloon                             8
Leonberg                            8
toy_terrier                         8
seashore                            8
Christmas_stocking                  8
Scottish_deerhound                  8
washbasin                           8
Irish_terrier                       8
badger                              8
wire-haired_fox_terrier             8
Sussex_spaniel                      8
snorkel                             8
Welsh_springer_spaniel              8
space_heater                        8
jigsaw_puzzle                       8
Arctic_fox                          8
hyena                               8
ox                                  8
patio                               8
hermit_crab                         8
bubble                              8
bow_tie                             8
komondor                            8
wallaby                             8
wood_rabbit                         8
Lhasa                               8
sea_lion                            8
gas_pump                            8
Ibizan_hound                        8
basenji                             8
cash_machine                        8
Rhodesian_ridgeback                 8
laptop                              8
giant_schnauzer                     8
brown_bear                          8
prison                              8
ski_mask                            8
guinea_pig                          8
sliding_door                        4
sorrel                              4
ostrich                             4
shower_curtain                      4
Scotch_terrier                      4
Gordon_setter                       4
pedestal                            4
Egyptian_cat                        4
African_crocodile                   4
Appenzeller                         4
dhole                               4
microwave                           4
china_cabinet                       4
sandbar                             4
otter                               4
maillot                             4
convertible                         4
bannister                           4
picket_fence                        4
slug                                4
tricycle                            4
tick                                4
starfish                            4
bow                                 4
rotisserie                          4
carton                              4
carousel                            4
triceratops                         4
toyshop                             4
bookcase                            4
piggy_bank                          4
beaver                              4
coral_reef                          4
meerkat                             4
stove                               4
shield                              4
remote_control                      4
paper_towel                         4
alp                                 4
fountain                            4
Brabancon_griffon                   4
vacuum                              4
basketball                          4
pool_table                          4
espresso                            4
groenendael                         4
park_bench                          4
mud_turtle                          4
axolotl                             4
frilled_lizard                      4
leopard                             4
bustard                             4
standard_schnauzer                  4
minibus                             4
cougar                              4
rapeseed                            4
quilt                               4
sunglasses                          4
mortarboard                         4
cup                                 4
wooden_spoon                        4
agama                               4
hummingbird                         4
bighorn                             4
coil                                4
American_black_bear                 4
dogsled                             4
silky_terrier                       4
canoe                               4
giant_panda                         4
crash_helmet                        4
dining_table                        4
birdhouse                           4
timber_wolf                         4
wild_boar                           4
bee_eater                           4
ice_lolly                           4
sulphur-crested_cockatoo            4
washer                              4
clumber                             4
lion                                4
terrapin                            4
grey_fox                            4
orange                              4
boathouse                           4
stone_wall                          4
lawn_mower                          4
earthstar                           4
Loafer                              4
tailed_frog                         4
African_grey                        4
white_wolf                          4
cowboy_boot                         4
long-horned_beetle                  4
cowboy_hat                          4
common_iguana                       4
grille                              4
water_buffalo                       4
hand_blower                         4
comic_book                          4
goose                               4
toilet_tissue                       4
nail                                4
hay                                 4
Japanese_spaniel                    4
hotdog                              4
radio_telescope                     4
leatherback_turtle                  4
cuirass                             4
marmot                              4
harp                                4
banana                              4
bison                               4
guenon                              4
barbell                             4
lynx                                4
tusker                              4
pitcher                             4
damselfly                           4
briard                              4
tabby                               4
weasel                              4
leaf_beetle                         4
snail                               4
prayer_rug                          4
ping-pong_ball                      4
skunk                               4
koala                               4
crane                               4
lakeside                            4
Name: p1, dtype: int64

df.shape

(5512, 22)

# lets look at the datatypes
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5512 entries, 0 to 5511
Data columns (total 22 columns):
tweet_id              5512 non-null int64
timestamp             5512 non-null object
source                5512 non-null object
text                  5512 non-null object
expanded_urls         5512 non-null object
rating_numerator      5512 non-null float64
rating_denominator    5512 non-null int64
name                  5512 non-null object
dog_stage             5512 non-null object
jpg_url               5512 non-null object
img_num               5512 non-null int64
p1                    5512 non-null object
p1_conf               5512 non-null float64
p1_dog                5512 non-null bool
p2                    5512 non-null object
p2_conf               5512 non-null float64
p2_dog                5512 non-null bool
p3                    5512 non-null object
p3_conf               5512 non-null float64
p3_dog                5512 non-null bool
retweet_count         5512 non-null int64
favorite_count        5512 non-null int64
dtypes: bool(3), float64(4), int64(5), object(10)
memory usage: 834.4+ KB

#summary statistics
df.describe()

df.rating_numerator.min(),df.rating_numerator.max()

(2.0, 14.0)

df.rating_denominator.min(),df.rating_denominator.max()

(10, 10)

df.dog_stage.value_counts()

puppo      1378
doggo      1378
pupper     1378
floofer    1378
Name: dog_stage, dtype: int64

plt.figure(figsize=[10,5])
base_color = sb.color_palette()[0]
df.dog_stage.value_counts().plot(kind='bar',color=base_color)
plt.ylabel('count')
plt.title('Counts of dog stages Documented');
plt.savefig("dog_stages.png", bbox_inches='tight')

plt.figure(1)
plt.subplot(131)

df['p1'].value_counts(normalize=True).head(10).plot.bar(color=base_color,figsize=(24,6), fontsize = 15.0)
plt.title('p1 - Algorithm Output', fontsize = 22.0)
plt.ylabel('Count %', fontsize = 20.0)

plt.subplot(132)
df['p2'].value_counts(normalize=True).head(10).plot.bar(color=base_color,figsize=(24,6), fontsize = 15.0)
plt.title('p2 - Algorithm Output', fontsize = 22.0)
plt.ylabel('Count %', fontsize = 20.0)

plt.subplot(133)
df['p3'].value_counts(normalize=True).head(10).plot.bar(color=base_color,figsize=(24,6), fontsize = 15.0)
plt.title('p3 - Algorithm Output', fontsize = 22.0)
plt.ylabel('Count %', fontsize = 20.0);
plt.suptitle('Top predicted Dog classes by Our 3 Algorithm',fontweight="bold", fontsize = 25.0)
plt.savefig("dogs_class.png", bbox_inches='tight')

sb.countplot(data=df[df.source != np.nan],x='source')

<matplotlib.axes._subplots.AxesSubplot at 0x7f73aef4eac8>

df.rating_denominator.shape

(5512,)

df.retweet_count.shape

(5512,)

plt.figure(figsize=[11,5])
plt.scatter(x='rating_numerator',y='retweet_count',data=df)
plt.xlabel('Rating')
plt.ylabel('Number of Retweets')
plt.savefig("scatter.png", bbox_inches='tight')

Findings for my analysis¶

It appeared that most of the rating were from iphone device
Also labrador_retriever was most predicted in our second and third algorithm.
four stages of dogs where documented.
the max numerator rating was 14

Limitation¶

more cleaning could been done in the name rows to make the names consistent.
the timestamp column can be explored further.

from subprocess import call
call(['python', '-m', 'nbconvert', 'wrangle_act.ipynb'])

0

	tweet_id	retweet_count	favorite_count
0	892420643555336193	8853	39467
1	892177421306343426	6514	33819
2	891815181378084864	4328	25461
3	891689557279858688	8964	42908
4	891327558926688256	9774	41048

	tweet_id	retweet_count	favorite_count
0	892420643555336193	8853	39467
1	892177421306343426	6514	33819
2	891815181378084864	4328	25461
3	891689557279858688	8964	42908
4	891327558926688256	9774	41048

	tweet_id	img_num	p1_conf	p2_conf	p3_conf
count	2.075000e+03	2075.000000	2075.000000	2.075000e+03	2.075000e+03
mean	7.384514e+17	1.203855	0.594548	1.345886e-01	6.032417e-02
std	6.785203e+16	0.561875	0.271174	1.006657e-01	5.090593e-02
min	6.660209e+17	1.000000	0.044333	1.011300e-08	1.740170e-10
25%	6.764835e+17	1.000000	0.364412	5.388625e-02	1.622240e-02
50%	7.119988e+17	1.000000	0.588230	1.181810e-01	4.944380e-02
75%	7.932034e+17	1.000000	0.843855	1.955655e-01	9.180755e-02
max	8.924206e+17	4.000000	1.000000	4.880140e-01	2.734190e-01

	tweet_id	timestamp	source	text	expanded_urls	rating_numerator	rating_denominator	name	dog_stage	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog	retweet_count	favorite_count
0	892420643555336193	2017-08-01 16:23:56	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13.0	10	Phineas	doggo	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False	8853	39467
1	892420643555336193	2017-08-01 16:23:56	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13.0	10	Phineas	floofer	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False	8853	39467
2	892420643555336193	2017-08-01 16:23:56	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13.0	10	Phineas	pupper	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False	8853	39467
3	892420643555336193	2017-08-01 16:23:56	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13.0	10	Phineas	puppo	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False	8853	39467
4	892177421306343426	2017-08-01 00:17:27	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13.0	10	Tilly	doggo	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	0.090647	True	papillon	0.068957	True	6514	33819
5	892177421306343426	2017-08-01 00:17:27	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13.0	10	Tilly	floofer	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	0.090647	True	papillon	0.068957	True	6514	33819
6	892177421306343426	2017-08-01 00:17:27	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13.0	10	Tilly	pupper	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	0.090647	True	papillon	0.068957	True	6514	33819
7	892177421306343426	2017-08-01 00:17:27	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13.0	10	Tilly	puppo	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	0.090647	True	papillon	0.068957	True	6514	33819
8	891815181378084864	2017-07-31 00:18:03	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12.0	10	Archie	doggo	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	0.078253	True	kelpie	0.031379	True	4328	25461
9	891815181378084864	2017-07-31 00:18:03	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12.0	10	Archie	floofer	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	0.078253	True	kelpie	0.031379	True	4328	25461
10	891815181378084864	2017-07-31 00:18:03	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12.0	10	Archie	pupper	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	0.078253	True	kelpie	0.031379	True	4328	25461
11	891815181378084864	2017-07-31 00:18:03	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12.0	10	Archie	puppo	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	0.078253	True	kelpie	0.031379	True	4328	25461
12	891689557279858688	2017-07-30 15:58:51	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	https://twitter.com/dog_rates/status/891689557...	13.0	10	Darla	doggo	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	0.168086	True	spatula	0.040836	False	8964	42908
13	891689557279858688	2017-07-30 15:58:51	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	https://twitter.com/dog_rates/status/891689557...	13.0	10	Darla	floofer	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	0.168086	True	spatula	0.040836	False	8964	42908
14	891689557279858688	2017-07-30 15:58:51	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	https://twitter.com/dog_rates/status/891689557...	13.0	10	Darla	pupper	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	0.168086	True	spatula	0.040836	False	8964	42908
15	891689557279858688	2017-07-30 15:58:51	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	https://twitter.com/dog_rates/status/891689557...	13.0	10	Darla	puppo	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	0.168086	True	spatula	0.040836	False	8964	42908
16	891327558926688256	2017-07-29 16:00:24	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	https://twitter.com/dog_rates/status/891327558...	12.0	10	Franklin	doggo	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	0.225770	True	German_short-haired_pointer	0.175219	True	9774	41048
17	891327558926688256	2017-07-29 16:00:24	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	https://twitter.com/dog_rates/status/891327558...	12.0	10	Franklin	floofer	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	0.225770	True	German_short-haired_pointer	0.175219	True	9774	41048
18	891327558926688256	2017-07-29 16:00:24	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	https://twitter.com/dog_rates/status/891327558...	12.0	10	Franklin	pupper	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	0.225770	True	German_short-haired_pointer	0.175219	True	9774	41048
19	891327558926688256	2017-07-29 16:00:24	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	https://twitter.com/dog_rates/status/891327558...	12.0	10	Franklin	puppo	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	0.225770	True	German_short-haired_pointer	0.175219	True	9774	41048

	tweet_id	rating_numerator	rating_denominator	img_num	p1_conf	p2_conf	p3_conf	retweet_count	favorite_count
count	5.512000e+03	5512.000000	5512.0	5512.000000	5512.000000	5.512000e+03	5.512000e+03	5512.000000	5512.000000
mean	7.471101e+17	10.749289	10.0	1.214804	0.600940	1.342720e-01	6.058816e-02	2978.603048	9137.338897
std	6.866533e+16	1.901640	0.0	0.580590	0.273094	1.008740e-01	5.144500e-02	4454.470748	11634.849731
min	6.664188e+17	2.000000	10.0	1.000000	0.044333	1.011300e-08	1.740170e-10	23.000000	0.000000
25%	6.812424e+17	10.000000	10.0	1.000000	0.368660	5.171310e-02	1.543210e-02	748.000000	2187.000000
50%	7.316454e+17	11.000000	10.0	1.000000	0.599557	1.202710e-01	4.922740e-02	1659.500000	4578.000000
75%	8.054874e+17	12.000000	10.0	1.000000	0.868511	1.978190e-01	9.466370e-02	3636.000000	12041.000000
max	8.924206e+17	14.000000	10.0	4.000000	1.000000	4.880140e-01	2.734190e-01	56625.000000	107956.000000

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
0	892420643555336193	NaN	NaN	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892420643...	13	10	Phineas	None	None	None	None
1	892177421306343426	NaN	NaN	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892177421...	13	10	Tilly	None	None	None	None
2	891815181378084864	NaN	NaN	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891815181...	12	10	Archie	None	None	None	None

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True

	tweet_id	retweet_count	favorite_count
count	2354	2354	2354
unique	2354	1724	2007
top	667509364010450944	3652	0
freq	1	5	179

	tweet_id	timestamp	source	text	expanded_urls	rating_numerator	rating_denominator	name	dog_stage	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog	retweet_count	favorite_count
0	892420643555336193	2017-08-01 16:23:56	iphone	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13.0	10	Phineas	doggo	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False	8853	39467
1	892420643555336193	2017-08-01 16:23:56	iphone	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13.0	10	Phineas	floofer	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False	8853	39467
2	892420643555336193	2017-08-01 16:23:56	iphone	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13.0	10	Phineas	pupper	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False	8853	39467
3	892420643555336193	2017-08-01 16:23:56	iphone	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13.0	10	Phineas	puppo	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False	8853	39467
4	892177421306343426	2017-08-01 00:17:27	iphone	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13.0	10	Tilly	doggo	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	0.090647	True	papillon	0.068957	True	6514	33819

	tweet_id	timestamp	source	text	expanded_urls	rating_numerator	rating_denominator	name	dog_stage	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog	retweet_count	favorite_count
0	892420643555336193	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13	10	Phineas	doggo	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	8.585110e-02	False	banana	7.611000e-02	False	8853	39467
1	892420643555336193	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13	10	Phineas	floofer	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	8.585110e-02	False	banana	7.611000e-02	False	8853	39467
2	892420643555336193	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13	10	Phineas	pupper	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	8.585110e-02	False	banana	7.611000e-02	False	8853	39467
3	892420643555336193	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	https://twitter.com/dog_rates/status/892420643...	13	10	Phineas	puppo	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	8.585110e-02	False	banana	7.611000e-02	False	8853	39467
4	892177421306343426	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13	10	Tilly	doggo	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	9.064650e-02	True	papillon	6.895690e-02	True	6514	33819
5	892177421306343426	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13	10	Tilly	floofer	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	9.064650e-02	True	papillon	6.895690e-02	True	6514	33819
6	892177421306343426	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13	10	Tilly	pupper	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	9.064650e-02	True	papillon	6.895690e-02	True	6514	33819
7	892177421306343426	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	https://twitter.com/dog_rates/status/892177421...	13	10	Tilly	puppo	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	9.064650e-02	True	papillon	6.895690e-02	True	6514	33819
8	891815181378084864	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12	10	Archie	doggo	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	7.825300e-02	True	kelpie	3.137890e-02	True	4328	25461
9	891815181378084864	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12	10	Archie	floofer	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	7.825300e-02	True	kelpie	3.137890e-02	True	4328	25461
10	891815181378084864	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12	10	Archie	pupper	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	7.825300e-02	True	kelpie	3.137890e-02	True	4328	25461
11	891815181378084864	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	https://twitter.com/dog_rates/status/891815181...	12	10	Archie	puppo	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	7.825300e-02	True	kelpie	3.137890e-02	True	4328	25461
12	891689557279858688	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	https://twitter.com/dog_rates/status/891689557...	13	10	Darla	doggo	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	1.680860e-01	True	spatula	4.083590e-02	False	8964	42908
13	891689557279858688	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	https://twitter.com/dog_rates/status/891689557...	13	10	Darla	floofer	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	1.680860e-01	True	spatula	4.083590e-02	False	8964	42908
14	891689557279858688	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	https://twitter.com/dog_rates/status/891689557...	13	10	Darla	pupper	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	1.680860e-01	True	spatula	4.083590e-02	False	8964	42908
15	891689557279858688	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	https://twitter.com/dog_rates/status/891689557...	13	10	Darla	puppo	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	1.680860e-01	True	spatula	4.083590e-02	False	8964	42908
16	891327558926688256	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	https://twitter.com/dog_rates/status/891327558...	12	10	Franklin	doggo	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	2.257700e-01	True	German_short-haired_pointer	1.752190e-01	True	9774	41048
17	891327558926688256	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	https://twitter.com/dog_rates/status/891327558...	12	10	Franklin	floofer	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	2.257700e-01	True	German_short-haired_pointer	1.752190e-01	True	9774	41048
18	891327558926688256	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	https://twitter.com/dog_rates/status/891327558...	12	10	Franklin	pupper	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	2.257700e-01	True	German_short-haired_pointer	1.752190e-01	True	9774	41048
19	891327558926688256	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	https://twitter.com/dog_rates/status/891327558...	12	10	Franklin	puppo	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	2.257700e-01	True	German_short-haired_pointer	1.752190e-01	True	9774	41048
20	891087950875897856	2017-07-29 00:08:17 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a majestic great white breaching ...	https://twitter.com/dog_rates/status/891087950...	13	10	None	doggo	https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg	1	Chesapeake_Bay_retriever	0.425595	True	Irish_terrier	1.163170e-01	True	Indian_elephant	7.690220e-02	False	3261	20562
21	891087950875897856	2017-07-29 00:08:17 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a majestic great white breaching ...	https://twitter.com/dog_rates/status/891087950...	13	10	None	floofer	https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg	1	Chesapeake_Bay_retriever	0.425595	True	Irish_terrier	1.163170e-01	True	Indian_elephant	7.690220e-02	False	3261	20562
22	891087950875897856	2017-07-29 00:08:17 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a majestic great white breaching ...	https://twitter.com/dog_rates/status/891087950...	13	10	None	pupper	https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg	1	Chesapeake_Bay_retriever	0.425595	True	Irish_terrier	1.163170e-01	True	Indian_elephant	7.690220e-02	False	3261	20562
23	891087950875897856	2017-07-29 00:08:17 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a majestic great white breaching ...	https://twitter.com/dog_rates/status/891087950...	13	10	None	puppo	https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg	1	Chesapeake_Bay_retriever	0.425595	True	Irish_terrier	1.163170e-01	True	Indian_elephant	7.690220e-02	False	3261	20562
24	890971913173991426	2017-07-28 16:27:12 +0000	<a href="http://twitter.com/download/iphone" r...	Meet Jax. He enjoys ice cream so much he gets ...	https://gofundme.com/ydvmve-surgery-for-jax,ht...	13	10	Jax	doggo	https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg	1	Appenzeller	0.341703	True	Border_collie	1.992870e-01	True	ice_lolly	1.935480e-01	False	2158	12041
25	890971913173991426	2017-07-28 16:27:12 +0000	<a href="http://twitter.com/download/iphone" r...	Meet Jax. He enjoys ice cream so much he gets ...	https://gofundme.com/ydvmve-surgery-for-jax,ht...	13	10	Jax	floofer	https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg	1	Appenzeller	0.341703	True	Border_collie	1.992870e-01	True	ice_lolly	1.935480e-01	False	2158	12041
26	890971913173991426	2017-07-28 16:27:12 +0000	<a href="http://twitter.com/download/iphone" r...	Meet Jax. He enjoys ice cream so much he gets ...	https://gofundme.com/ydvmve-surgery-for-jax,ht...	13	10	Jax	pupper	https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg	1	Appenzeller	0.341703	True	Border_collie	1.992870e-01	True	ice_lolly	1.935480e-01	False	2158	12041
27	890971913173991426	2017-07-28 16:27:12 +0000	<a href="http://twitter.com/download/iphone" r...	Meet Jax. He enjoys ice cream so much he gets ...	https://gofundme.com/ydvmve-surgery-for-jax,ht...	13	10	Jax	puppo	https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg	1	Appenzeller	0.341703	True	Border_collie	1.992870e-01	True	ice_lolly	1.935480e-01	False	2158	12041
28	890729181411237888	2017-07-28 00:22:40 +0000	<a href="http://twitter.com/download/iphone" r...	When you watch your owner call another dog a g...	https://twitter.com/dog_rates/status/890729181...	13	10	None	doggo	https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg	2	Pomeranian	0.566142	True	Eskimo_dog	1.784060e-01	True	Pembroke	7.650690e-02	True	16716	56848
29	890729181411237888	2017-07-28 00:22:40 +0000	<a href="http://twitter.com/download/iphone" r...	When you watch your owner call another dog a g...	https://twitter.com/dog_rates/status/890729181...	13	10	None	floofer	https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg	2	Pomeranian	0.566142	True	Eskimo_dog	1.784060e-01	True	Pembroke	7.650690e-02	True	16716	56848
30	890729181411237888	2017-07-28 00:22:40 +0000	<a href="http://twitter.com/download/iphone" r...	When you watch your owner call another dog a g...	https://twitter.com/dog_rates/status/890729181...	13	10	None	pupper	https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg	2	Pomeranian	0.566142	True	Eskimo_dog	1.784060e-01	True	Pembroke	7.650690e-02	True	16716	56848
31	890729181411237888	2017-07-28 00:22:40 +0000	<a href="http://twitter.com/download/iphone" r...	When you watch your owner call another dog a g...	https://twitter.com/dog_rates/status/890729181...	13	10	None	puppo	https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg	2	Pomeranian	0.566142	True	Eskimo_dog	1.784060e-01	True	Pembroke	7.650690e-02	True	16716	56848
32	890609185150312448	2017-07-27 16:25:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Zoey. She doesn't want to be one of th...	https://twitter.com/dog_rates/status/890609185...	13	10	Zoey	doggo	https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg	1	Irish_terrier	0.487574	True	Irish_setter	1.930540e-01	True	Chesapeake_Bay_retriever	1.181840e-01	True	4429	28226
33	890609185150312448	2017-07-27 16:25:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Zoey. She doesn't want to be one of th...	https://twitter.com/dog_rates/status/890609185...	13	10	Zoey	floofer	https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg	1	Irish_terrier	0.487574	True	Irish_setter	1.930540e-01	True	Chesapeake_Bay_retriever	1.181840e-01	True	4429	28226
34	890609185150312448	2017-07-27 16:25:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Zoey. She doesn't want to be one of th...	https://twitter.com/dog_rates/status/890609185...	13	10	Zoey	pupper	https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg	1	Irish_terrier	0.487574	True	Irish_setter	1.930540e-01	True	Chesapeake_Bay_retriever	1.181840e-01	True	4429	28226
35	890609185150312448	2017-07-27 16:25:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Zoey. She doesn't want to be one of th...	https://twitter.com/dog_rates/status/890609185...	13	10	Zoey	puppo	https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg	1	Irish_terrier	0.487574	True	Irish_setter	1.930540e-01	True	Chesapeake_Bay_retriever	1.181840e-01	True	4429	28226
36	890240255349198849	2017-07-26 15:59:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Cassie. She is a college pup. Studying...	https://twitter.com/dog_rates/status/890240255...	14	10	Cassie	doggo	https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg	1	Pembroke	0.511319	True	Cardigan	4.510380e-01	True	Chihuahua	2.924820e-02	True	7711	32467
37	890240255349198849	2017-07-26 15:59:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Cassie. She is a college pup. Studying...	https://twitter.com/dog_rates/status/890240255...	14	10	Cassie	floofer	https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg	1	Pembroke	0.511319	True	Cardigan	4.510380e-01	True	Chihuahua	2.924820e-02	True	7711	32467
38	890240255349198849	2017-07-26 15:59:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Cassie. She is a college pup. Studying...	https://twitter.com/dog_rates/status/890240255...	14	10	Cassie	pupper	https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg	1	Pembroke	0.511319	True	Cardigan	4.510380e-01	True	Chihuahua	2.924820e-02	True	7711	32467
39	890240255349198849	2017-07-26 15:59:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Cassie. She is a college pup. Studying...	https://twitter.com/dog_rates/status/890240255...	14	10	Cassie	puppo	https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg	1	Pembroke	0.511319	True	Cardigan	4.510380e-01	True	Chihuahua	2.924820e-02	True	7711	32467